In so-called “infrastructureless” traffic tolling systems, mobile network-based onboard units determine their own positions by means of, e.g., a global navigation satellite system (GNSS) and communicate tolling information based on the determined positions to a tolling server via the mobile network. The onboard units can be either of “thin” or “thick” client type. Thin client onboard units communicate raw or pre-processed position fixes directly to a tolling server or proxy, where the positional data is analyzed by a map matcher or a virtual gantry detection algorithm to determine if the vehicle has used any tollable area. Thick client onboard units are equipped with their own map matcher and autonomously determine whether they use a toll area or not by comparing the GNSS position fixes with map data stored in the map matcher or algorithm. Both types of onboard units are thus able to be used on roads having no infrastructure at all that is related to tolling.
One of the problems encountered in such a system is the detection of vehicles not using onboard units or malfunctioning onboard units. To this end, surveillance stations can be set up that record pictures of passing vehicles and perform an OCR (Optical Character Recognition) process on them to retrieve the vehicle's license plate number (LPN). The license plate number can then be compared with the tolling information the onboard units have sent via the mobile network to the tolling server. All pictures taken have to be stored in local or central databases as evidence against drivers of vehicles that did not use any onboard unit or declared their positions incorrectly. The databases thus have to store an enormous amount of data stemming from the pictures taken, and the OCR-reading process takes up huge computational resources.